In this review paper, we aim to describe the potential for, and the key challenges to, applying PES projects to mangroves. By adopting a ''carbocentric approach,'' we show that mangrove forests are strong candidates for PES projects. They are particularly well suited to the generation of carbon credits because of their unrivaled potential as carbon sinks, their resistance and resilience to natural hazards, and their extensive provision of Ecosystem Services other than carbon sequestration, primarily nursery areas for fish, water purification and coastal protection, to the benefit of local communities as well as to the global population. The voluntary carbon market provides opportunities for the development of appropriate protocols and good practice case studies for mangroves at a small scale, and these may influence larger compliance schemes in the future. Mangrove habitats are mostly located in developing countries on communally or state-owned land. This means that issues of national and local governance, land ownership and management, and environmental justice are the main challenges that require careful planning at the early stages of mangrove PES projects to ensure successful outcomes and equitable benefit sharing within local communities.

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Variance-based sensitivity analysis of a wind risk model-model behaviour and lessons for forest modelling Highlights:  The Sobol' method for correlated variables is applied to a complex wind-risk model.  The results are interpreted from the viewpoints of model users and modellers.  The variance-based approach is sensitive to the variables correlation structure.  Rooting depth and soil type provide minor contribution to the outputs variance.  ForestGALES models the dynamics of wind damage to forest stands very effectively.

Through changes in policy and practice, the inherent intent of the ecosystem services (ES) concept is to safeguard ecosystems for human wellbeing. While impact is intrinsic to the concept, little is known about how and whether ES science leads to impact. Evidence of impact is needed. Given the lack of consensus on what constitutes impact, we differentiate between attributional impacts (transitional impacts on policy, practice, awareness or other drivers) and consequential impacts (real, on-the-ground impacts on biodiversity, ES, ecosystem functions and human wellbeing) impacts. We conduct rigorous statistical analyses on three extensive databases for evidence of attributional impact (the form most prevalently reported): the IPBES catalogue (n = 102), the Lautenbach systematic review (n = 504) and a 5-year in-depth survey of the OPERAs Exemplars (n = 13). To understand the drivers of impacts, we statistically analyse associations between study characteristics and impacts. Our findings show that there exists much confusion with regard to defining ES science impacts, and that evidence of attributional impact is scarce: only 25% of the IPBES assessments self-reported impact (7% with evidence); in our meta-analysis of Lautenbach's systematic review, 33% of studies provided recommendations indicating intent of impacts. Systematic impact reporting was imposed by design on the OPERAs Exemplars: 100% reported impacts, suggesting the importance of formal impact reporting. The generalised linear models and correlations between study characteristics and attributional impact dimensions highlight four characteristics as minimum baseline for impact: study robustness, integration of policy instruments into study design, stakeholder involvement and type of stakeholders involved. Further in depth examination of the OPERAs Exemplars showed that study characteristics associated with impact on awareness and practice differ from those associated with impact on policy: to achieve impact along specific dimensions, bespoke study designs are recommended. These results inform targeted recommendations for ES science to break its impact glass ceiling.

<p>The risk of wind damage to European forests is expected to increase due to the changed climate. Therefore, research efforts in forestry have been focussing on the development of analytical and modelling tools to improve the prediction of forests' susceptibility to wind damage, and ultimately to support forest management decisions in increasing wind resistance in forest stands. Recent catastrophic wind damage to European forests has shown that wind damage risk applies also to montane forests. Some of them are of particular importance for the various ecosystem services they provide, including protection from gravitational hazards and defence against soil erosion. At present, the available forest wind risk models have been tested and used mainly on production or planted forests in different countries, but never in the complexity of mountainous terrains. The aim of this study is to introduce a methodology for the validation of a new parametrization of ForestGALES wind risk model for the alpine environment. The parameterisation was developed through field tests (e.g., pulling tests on trees) and validated based on the observed wind damage caused by the storm Vaia, which occurred in northern Italy in October 2018, and the pre-disturbance forest characteristics. The use of this parameterisation can allow the construction of wind vulnerability maps starting from LiDAR data. Mapping vulnerability to natural disturbances, in this case, wind, is an essential tool for forest planning and management. The frequency of natural disturbances is expected to increase, as is their severity and forest management needs to target interventions to obtain more resistant and resilient forest stands. Management should aim to apply strategies to prevent future damage in a way that ensures continued protective effectiveness, guaranteeing the preservation of local communities and infrastructures.</p>

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<p>The interaction of forests and wind disturbances is a topical issue in scientific research, especially considering that the ongoing climate change will lead to a probable increase in the frequency of natural disturbances of high severity (e.g., storms).</p><p>The study of wind-tree interaction has led to the development of various models for predicting wind risk damage to forest stands. Of these models, ForestGALES is the most widely adopted across forest species and geographical locations. Initially developed in the UK as a management tool to assess the susceptibility of plantations to windstorm damage, this semi-empirical, process-based wind risk model has since been expanded and used in other contexts, both European and non-European. Recently ForestGALES has been updated and developed in the R framework (fgr package), in order to be easily applicable to different scenarios. However, the original ForestGALES reference database used to derive empirical coefficients of tree anchorage is limited to a relatively flat area and small size trees (Diameter at Breast Height -DBH- less than 30 cm).</p><p>In this context, the first objective of this research was to investigate the anchorage of standing trees with large diameters by means of pulling tests. Therefore, 44 spruce trees (<em>Picea abies</em> (L.) Karst.), an important species for alpine silviculture and particularly susceptible to wind damage, were subjected to destructive pulling tests.</p><p> Using a load cell, inclinometers and strain gauges the tree felling was monitored in all its phases. Of the 44 plants tested (DBH> 40 cm), 13 were selected in sloped terrain in order to test if slope may affect stability, in a comparison with trees with similar characteristics on flat terrain. The first results showed that trees on a slope have a higher overturning coefficient and are therefore more resistant to uprooting.</p><p>The data obtained from the field were translated into input parameters for ForestGALES model, allowing to differentiate the parameters for spruce according to the slope of the terrain. The parametrisation was further complemented with physical parameters (MOE and MOR) typical of spruce trees grown in the mountain/dolomitic environment. Using these new parametrisations, wind risk assessment maps were created for a case study area located in the north-eastern Italian Alps. This area was strongly affected by storm Vaia in October 2018, the mapping, therefore, aims to observe the susceptibility of stands before and after the disturbance event.</p>